Research on Smart City Traffic Flow Optimization Based on Hidden Markov Models

Mingjie Zhou

doi:10.47297/taposatWSP2633-456923.20260701

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Research on Smart City Traffic Flow Optimization Based on Hidden Markov Models

Volume 7, Issue 1

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Research on Smart City Traffic Flow Optimization Based on Hidden Markov Models
Research on Smart City Traffic Flow Optimization Based on Hidden Markov Models
Universiti Sains Malaysia, Penang, 11800, Malaysia
Vol. 7, Issue 1, Pages: 110-113(2026)
Published：30 January 2026
DOI：10.47297/taposatWSP2633-456923.20260701
Accepted：

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Zhou Mingjie.Research on Smart City Traffic Flow Optimization Based on Hidden Markov Models[J].Theory and Practice of Science and Technology,2026,07(01):110-113.
DOI：

Zhou Mingjie.Research on Smart City Traffic Flow Optimization Based on Hidden Markov Models[J].Theory and Practice of Science and Technology,2026,07(01):110-113. DOI： 10.47297/taposatWSP2633-456923.20260701.

摘要

Abstract

With the continuous advancement of smart transportation development and the "Transportation Powerhouse" strategy

traffic flow forecasting plays a crucial role in alleviating traffic congestion and enhancing road resource utilization efficiency. Among existing traffic flow prediction methods

traditional statistical models struggle to capture nonlinear characteristics

while machine learning models face challenges such as high data requirements and parameter sensitivity. Furthermore

Furthermore

existing studies lack systematic model integration and comprehensive comparative analysis. This paper focuses on Hidden Markov Models (HMMs)

employing literature review and comparative analysis to systematically examine the application progress and technical characteristics of traditional statistical models

machine learning models

and HMMs in traffic flow forecasting since 1990. Findings indicate that HMMs effectively capture both the randomness and temporal patterns of traffic flows

demonstrating superior stability and adaptability in short-term traffic flow prediction and congestion state identification.Improved HMMs effectively mitigate overfitting through state number optimization and algorithmic enhancements

further elevating prediction accuracy in complex traffic scenarios. The conclusions of this study provide guidance for the rational selection of traffic flow prediction models and lay a theoretical foundation for subsequent research on multi-factor integrated HMM optimization.

关键词

Keywords

references

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